A novel CFD based method for assessing the coronary microvascular dysfunction: combining coronary angiography and routine fractional flow reserve Running Title: CFD based method for assessing the CMD

doi:10.21203/rs.3.rs-3990407/v1

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Research Article

A novel CFD based method for assessing the coronary microvascular dysfunction: combining coronary angiography and routine fractional flow reserve Running Title: CFD based method for assessing the CMD

https://doi.org/10.21203/rs.3.rs-3990407/v1

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Background

A novel computational fluid dynamics (CFD) based method was proposed for the measurement of intracoronary mean flow rate (Q) and absolute microvascular resistance (AMR) by combining coronary angiography and routine fractional flow reserve (FFR) measurement. The aim of this study is to explore the potential of the proposed method to assess the presence of coronary microvascular dysfunction (CMD).

Methods

Patients without hemodynamically significant coronary artery stenosis or those after a successful PCI were enrolled. Rest SPECT myocardial perfusion imaging (MPI) was employed as evidence for assessing the presence of CMD. Based on the sum rest score of target vessel (SRSTV), patients were divided into normal MPI group (SRSTV ≤ 1) and abnormal MPI group (SRSTV > 1). Q and AMR were obtained with a CFD based method during rest and hyperemia condition, and receiver operating characteristic (ROC) curve analyses were performed for discriminating patients with normal MPI and with abnormal MPI.

Results

A total of 57 patients with 61 target vessels were investigated. Based on SRSTV, 22 patients (38.6%) with 23 target vessels (37.7%) were associated with abnormal MPI. Compared with abnormal MPI group, the AMR was significantly lower in normal MPI group (1.97 ± 1.50 mmHg·s/ml vs 3.41 ± 2.30 mmHg·s/ml, p = 0.011), and the Q was significant higher in normal MPI group (0.94 ± 70 mmHg·s/ml vs 0.56 ± 0.50 mmHg·s/ml, p = 0.025). According to ROC curve analysis, the ROC-AUC of Q and AMR for discriminating normal MPI and abnormal MPI was 0.720(95% CI: 0.591–0.828, p = 0.001) and 0.731(95% CI: 0.602–0.836, p = 0.001).

Conclusions

The new CFD based method provides an easy and accurate way to measure the intracoronary flow rate and absolute microvascular resistance, and constitutes a new opportunity for assessing the presence of CMD.

coronary microvascular resistance

coronary angiography

fractional flow reserve

CFD

Hemodynamics

Coronary microvascular is the main site of regulation of coronary blood flow (CBF). Coronary microvascular dysfunction (CMD) impairs the ability of microvascular to regulate CBF in response to changes of myocardial oxygen demand and, if severe enough lead to myocardial ischemia[1]. Evidence supports that CMD is associated with nonischemic cardiomyopathies[2, 3], cardiometabolic disease[4], and likely plays an important role in the pathophysiology of heart failure with preserved ejection fraction (HFpEF)[5, 6]. Currently, a variety of approaches are established for assessing the presence of CMD, varying from indirect non-invasive measurements to invasive coronary hemodynamic measurement[7, 8]. Among them, coronary microvascular resistance (cMVR) has emerged as the most promising indicator given its ability to directly quantify the microvascular dilatory function. During maximum hyperemia, the backward pressure of coronary venous is negligible, thus the pressure gradient of coronary microvascular approximately equals to the distal arterial pressure (Pd). According to the Ohm's law, cMVR can be assessed as the index of Pd divided by the flow rate (absolute microvascular resistance, AMR). In catheter laboratory, pressure and flow can be measured simultaneously with dual-sensor wires equipped with pressure sensor and thermodilution or Doppler sensor[9]. With these techniques, index of microvascular resistance (IMR) and hyperemic microvascular resistance (HMR) are defined to assess the presence of CMD[10–12]. However, dual-sensor wires greatly increase the complexity of the measurements and its application within clinical practice remains limited[13].

Recently, Morris et al. proposed a novel CFD based method which combined anatomy and functional information to calculated the mean flow rate[14]. In this approach, 3D model of the epicardial coronary artery was reconstructed from multi-view coronary angiography images; then, with the measured proximal and distal mean pressure during FFR measurement, the corresponding mean flow rate was calculated by iteratively solving of the Navier-Stokes equations[14]. With this approach, the absolute flow rate (Q) and AMR can be obtained with routine fractional flow reserve (FFR) measurement. Because the measurement of intracoronary pressure is simple, accurate and broadly applied in catheterization laboratory, this new approach holds great potential for clinical utilization. However, routine FFR measurement only records the distal pressure at target vessel, resulting that the side branches are ignored in the CFD model (lacking of boundary conditions to include side branches). This simplification has great impact on the computed flow rate[15]. To overcome this limitation, our group proposed a novel CFD method which implicitly specifying constraints for splitting flow between branches within a pressure-flow curve based flow resistance model[15]. With this approach, the mean intracoronary blood flow rate can be calculated in two or more branches by only recording the distal pressure at target vessel. Although simulation experiments have proved that this method improves greatly the accuracy of the calculated mean blood flow rate, its feasibility and value in routine clinical application are still unknown. In this paper, we aimed to demonstrate the potential value of this novel method for assessing cMVR by comparing with SPECT myocardial perfusion imaging (MPI).

A total of 57 patients with 61 target vessels (37 without hemodynamically significant CAD, 24 after a successful PCI) were included in the current analysis. Out of the 61 target vessels, 45 (73.8%) in LAD, 6 (9.8%) in LCX, and 10 (16.4%) in RCA. Based on SRSTV, 22 patients (38.6%) with 23 target vessels (37.7%) were associated with abnormal MPI. Further baseline variables for the included patients in normal MPI group and abnormal MPI group were described in Table 1.

Table 1

Baseline Characteristics.
	Normal group (n = 38)	Abnormal group (n = 23)	P Value
Age (Year)	60.41 ± 12.16	63.23 ± 10.94	0.387
Males (male, %)	28(73.68)	19(82.61)	0.422
BMI (kg/m²)	24.88 ± 3.00	23.93 ± 3.24	0.272
SBP(mmHg)	133.79 ± 20.87	131.73 ± 23.85	0.736
DBP(mmHg)	75.30 ± 11.95	74.55 ± 12.89	0.824
Heart rate (bpm)	73.48 ± 11.14	77.14 ± 11.95	0.258
Hospitalization days (Day)	4.19 ± 1.64	5.10 ± 1.90	0.069
History of smoking (%)	15(39.47)	7(30.43)	0.312
History of drinking (%)	4(10.53)	4(17.39)	0.485
Hypertension (%)	14(36.84)	9(39.13)	0.911
Diabetes (%)	12(31.58)	8(34.78)	1.000
Previous MI (%)	1(2.63)	1(4.35)	0.786
Previous PCI (%)	14(36.84)	13(56.52)	0.226
Previous CABG (%)	0	0
Family history of cardiovascular disease (%)	0	1(4.35)	0.216
ALT(U/L)	26.53 ± 16.49	26.59 ± 19.70	0.990
AST(U/L)	22.84 ± 7.97	23.00 ± 8.25	0.945
Creatinine(µmoI/L)	70.06 ± 15.43	76.75 ± 20.11	0.186
TC(mmol/L)	3.46 ± 1.12	3.04 ± 0.83	0.138
TG(mmol/L)	1.87 ± 1.21	1.50 ± 0.56	0.044
LDL-C(mmol/L)	1.82 ± 0.83	1.50 ± 0.56	0.113
HDL-C(mmol/L)	1.11 ± 0.33	1.06 ± 0.24	0.512
FBG(mmol/L)	6.38 ± 2.96	5.31 ± 1.02	0.223
cTnI(µg/L)	0.01 ± 0.02	0.01 ± 0.02	0.832
NT-proBNP(ng/L)	76.57(37.91, 144.03)	63.88(160.10, 500.90)	0.035
NYHA functional class			0.834
Ⅰ	4(10.53)	5(21.74)
Ⅱ	29(76.32)	13(56.52)
Ⅲ	4(10.53)	5(21.74)
Ⅳ	1(2.63)	0
LVEF(%)	56.06 ± 5.49	54.64 ± 7.12	0.413
Aspirin (%)	36(94.7)	20(86.96)	0.338
Clopidogrel (%)	8(21.05)	6(26.09)	0.648
Ticagrelor (%)	18(47.37)	14(60.87)	0.440
Statins (%)	38(100)	23(100)
Proton pump inhibitor (%)	31(81.58)	18(78.3)	0.680
ACEI/ARB(%)	18(47.37)	10(43.48)	0.686
Beta blockers (%)	33(86.84)	16(69.57)	0.074
Cardiotonic diuretic (%)	2(5.26)	3(13.04)	0.338
BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; ALT = Alanine transaminase; AST = Aspartate transaminase; FBG = fasting blood glucose; TC = total cholestrol; TG = triglyceride; HDL-C = high density lipoprotein cholestrol; LDL-C = low density lipoprotein cholestrol; NT-proBNP = N-terminal pro-brain natriureticpeptide; cTnI = Cardiac troponin I; LVEF = left ventricular ejection fraction.

As showed in Table 2, no difference was observed for FFR between normal MPI group and abnormal MPI group. The Q was significant higher in normal MPI group during both rest and hyperemia condition as compared with abnormal MPI group (Q: 0.94 ± 70 mmHg·s/ml vs 0.56 ± 0.50 mmHg·s/ml, p = 0.025; Q_rest: 0.53 ± 0.52 ml/min vs 0.30 ± 0.21 ml/min, p = 0.019) (Fig. 2). The AMR was significantly lower in normal MPI group (1.97 ± 1.50 mmHg·s/ml vs 3.41 ± 2.30 mmHg·s/ml, p = 0.011) during hyperemia condition, but no difference was observed during rest condition. Also, there were no difference in CFR_CFD and RRR_CFD between the two groups.

Table 2

Comparation of the hemodynamic parameters between normal MPI group and abnormal MPI group.
		Normal MPI group (n = 38)	Abnormal MPI group (n = 23)	P value
Target vessel (LAD/LCX/RCA)		27/3/8	18/3/2
	FFR	0.84 ± 0.07	0.83 ± 0.08	0.431
Absolute flow rate and microvascular resistance
	Q (ml/min)	0.94 ± 0.70	0.56 ± 0.50	0.025
	Q_rest (ml/min)	0.53 ± 0.52	0.30 ± 0.21	0.019
	AMR (mmHg·s/ml)	1.97 ± 1.50	3.41 ± 2.30	0.011
	AMR_rest (mmHg·s/ml)	6.56 ± 6.29	9.55 ± 8.01	0.109
CFR_CFD		2.22 ± 1.00	1.94 ± 0.53	0.151
RRR_CFD		3.21 ± 1.31	2.71 ± 0.80	0.070
Q: mean flow rate; Q_rest: mean flow rate during rest condition; AMR: absolute microvascular resistance; AMR_rest: absolute microvascular resistance during rest condition; CFR_CFD: CFD-based coronary flow reserve; RRR_CFD: CFD-based coronary resistive reserve ratio.

According to ROC curve analysis (Table 3 and Fig. 3), the ROC-AUC of Q, Q_rest and AMR for discriminating normal MPI and abnormal MPI was 0.720(95% CI: 0.591–0.828, p = 0.001), 0.656(95% CI: 0.523–0.773, p = 0.031) and 0.731(95% CI: 0.602–0.836, p = 0.001). With the best cutoff values chosen for Q (0.42 ml/min), the sensitivity and specificity for discriminating normal MPI and abnormal MPI were 60.9% and 81.6%. With the best cutoff values chosen for AMR (2.32 mmHg·s/ml), the sensitivity and specificity for discriminating normal MPI and abnormal MPI were 60.9% and 78.9%. The FFR, AMR_rest, CFR_CFD and RRR_CFD does not have the ability to distinguish between the two groups.

Table 3

ROC analysis for discriminating normal MPI and abnormal MPI.
	AUC	95%CI	P value
FFR	0.571	0.438–0.697	0.352
Q (ml/min)	0.720	0.591–0.828	0.001
Q_rest (ml/min)	0.656	0.523–0.773	0.031
AMR (mmHg·s/ml)	0.731	0.602–0.836	0.001
AMR_rest (mmHg·s/ml)	0.634	0.501–0.754	0.067
CFR_CFD	0.562	0.429–0.689	0.403
RRR_CFD	0.591	0.458–0.715	0.219
AUC, area under curve; CI, confidence interval; Q: mean flow rate; Q_rest: mean flow rate during rest condition; AMR: absolute microvascular resistance; AMR_rest: absolute microvascular resistance during rest condition; CFR_CFD: CFD-based coronary flow reserve; RRR_CFD: CFD-based coronary resistive reserve ratio.

Study population

Patients (aged at least 18 years) referred for diagnostic ICA and FFR in the Department of Cardiology, Xijing Hospital, Fourth Military Medical University between November 2020 and October 2022 due to stable or unstable angina were screened, and those who also underwent rest SPECT-MPI within 1 month were identified. Exclusion criteria included: 1) patients with acute myocardial infarctions (ST elevation or non–ST elevation), 2) iodine contrast agent allergy, 3) patients with high risk of bleeding diathesis or coagulation disorder such as malignancy, 4) patients with anemia, infectious disease or severe pulmonary disease, 5) patient with severe ventricular arrhythmias and obvious hemodynamic fluctuations. Angiographic exclusion criteria included poor angiographic image quality and severe vascular overlap. Enrolled patients were divided into 2 groups: those without hemodynamically significant CAD (defined as FFR > 0.80), or those after a successful PCI (defined as post-PCI FFR > 0.80) in patients with a significant CAD. In patients undergoing PCI, ICA and FFR used to derive Q and AMR were performed before the PCI, and the rest SPECT-MPI were performed after the successful PCI, eliminating the impact of epicardial coronary arteries.

FFR measurement

Ischemic potential of coronary stenoses was evaluated by FFR using QUANTEIN and PRESSUREWIRE (Abbott Global Health Care, USA). The instrument was zeroed three times in vitro, including the cath chamber pressure channel, aortic pressure and pressure guide wire pressure. Then, the aortic pressure and pressure guide wire pressure were equalized to the same baseline. Pressure wire was pushed through lesions and located at 2-3cm distal to the lesion. After the pressure curve waveform is stable, triphosadenine was continuously pumped through the coronary artery or vein to make the coronary micro vessels reach the maximum state of hyperemia. Finally, the pressure and FFR value were recorded.

Reconstruction of the 3D anatomy model of epicardial coronary artery

Coronary angiography images from multiple views were recorded at 15 frames per second. 3D centerlines of the targeted vessel and its large branches were reconstructed from two angiographic projections (separated by ≥ 25^o) using the point-cloud based approach[16], then circular luminal contours were fitted along all vessel paths to generate the 3D anatomy models.

CFD based computational model

The pressure drop of the epicardial coronary artery can be approximately modeled by a common fluid dynamic equation[17]:

$${P}_{a}-{P}_{d}=VF\bullet Q+EL\bullet {Q}^{2}$$

where P_a is the mean proximal pressure, P_d is the mean distal pressure, Q is the absolute mean flow rate, VF and EL are flow resistance parameters of epicardial coronary artery, namely viscous friction (VF) and expansion loss (EL). These two flow resistance parameters (VF and EL) are mainly determined by the anatomy of epicardial coronary artery, and can be obtained by a CFD-based pressure-flow curve method from the reconstructed 3D anatomy model, which was proposed by our group previously[15, 18]. Then, with the measured P_a and P_d during routine FFR measurement, the corresponding Q can be calculated through Eq. (1). The AMR is calculated as:

$$AMR=\frac{{P}_{d}}{Q}$$

To provide a full understanding of those computed hemodynamic parameters, the mean flow rate and absolute microvascular resistance during rest condition were also calculated (Q_rest, and AMR_rest). Based on the definition of coronary flow reserve (CFR) and coronary resistive reserve ratio (RRR), the CFD based CFR (CFR_CFD) and RRR (RRR_CFD) were also provided.

SPECT MPI

SPECT MPI was performed using Symbia T6 (Siemens Medical Solutions, USA). In the resting state, 99mtC-MIBI 20mCi was injected intravenously, and gated SPECT MPI was performed 1 hour later by electrocardiographic R-wave trigger. 32 original images were captured at 180 degrees at 8 frames per cardiac cycle. After computer processing, the CT images of short, vertical and horizontal axis of heart and the motion images of ventricular wall were reconstructed. Experienced clinicians scored perfusion abnormalities using a 5-point scale (0-normal, 1-mild, 2-moderate, 3-severe, 4-absent) and 17-segment model. Based on the territories of the targeted vessel (LAD, LCX or RCA), the summed rest score of target vessel (SRSTV) was calculated, and patients were divided into two groups: normal MPI group (SRSTV < = 1) and abnormal MPI group (SRSTV > 1).

Statistical analysis

The IBM-SPSS Statistics 25.0 software were used for all calculations. The continuous variables were presented as mean ± SD or as medians and interquartile ranges. The categorical variables were presented as percentages. The continuous variables were compared using the t test or Mann-Whitney U test where appropriate, and categorical variables were compared using the chi-square or fisher’s exact test. Receiver operating characteristic (ROC) curve analyses were performed for discriminating normal MPI and abnormal MPI and the best cutoff values for these variables were determined by Youden’s index.

The present study reported a novel CFD-based method to quantify the coronary microvascular dilatory function with routine coronary angiography and FFR measurements. The proposed method was validated in patients without obstructive epicardial stenosis (FFR > 0.8 or post-PCI FFR > 0.80 (after a successful PCI)). To increase the number of patients, two types of patients (without significant CAD or with significant CAD and after a successful PCI) were enrolled. The comparation of the computed hemodynamic parameters in these two types of patients were showed in Table 4, and no difference was observed between these two groups. Since the epicardial coronary arteries had limited impact on the flow rate in these two types of patients, the SPECT MPI was employed as evidence for assessing the presence of CMD. Based on the territories of the targeted vessel, SRSTV was calculated and the target vessels with normal microvascular (normal MPI) and abnormal microvascular (abnormal MPI) were identified. Results demonstrated that the proposed CFD-based method provided a convenient way to measure the intracoronary Q and AMR, and constitute a new opportunity for assessing the presence of CMD.

Table 4

The comparation of the computed hemodynamic parameters between patients without significant CAD or with significant CAD and after a successful PCI.
		without significant CAD(n = 37)	with significant CAD and after a successful PCI (n = 24)	P value
SRSTV		2.11 ± 3.43	1.38 ± 1.91	0.344
	Q (ml/min)	0.82 ± 0.53	0.76 ± 0.28	0.692
	Q_rest (ml/min)	0.46 ± 0.50	0.41 ± 0.35	0.602
	AMR (mmHg·s/ml)	2.66 ± 2.11	2.28 ± 1.72	0.444
	AMR_rest (mmHg·s/ml)	7.60 ± 7.19	7.81 ± 7.06	0.911
	CFR_CFD	2.04 ± 0.87	2.24 ± 0.85	0.371
	RRR_CFD	0.81 ± 1.18	3.33 ± 1.08	0.084
SRSTV: summed rest score of target vessel; Q: mean flow rate; Q_rest: mean flow rate during rest condition; AMR: absolute microvascular resistance; AMR_rest: absolute microvascular resistance during rest condition; CFR_CFD: CFD-based coronary flow reserve; RRR_CFD: CFD-based coronary resistive reserve ratio.

CMD is typically the underlying mechanism in patients with angina despite normal epicardial coronary arteries[2, 19]. Besides, CMD often coexists with CAD and contributes to myocardial ischemia[20, 21]. In a recent evaluation of patients attending the catheter laboratory with chest pain, 68% had evidence of CMD with no obstructive CAD, and 39–53% had evidence of coexisted CMD and epicardial disease[21]. However, since coronary microvascular is too small to be directly visualized, its assessment remains challenging. In order to assess cMVR, intracoronary pressure and flow should be measured simultaneously. Measurement of intracoronary pressure is simple and accurate, but measurement of intracoronary flow is much more challenging. Intracoronary wires equipped with Doppler sensor allow measuring the velocities of red cells [22]. The average peak velocity (APV), as a surrogate of flow rate, was quantitated. However, APV would be greatly affected by the insertion of Doppler sensor and the sensor angulation relative to the flow vector also has great impact on the measured velocity. In clinical practice, it is quite difficult to maintained the Doppler ultrasound beam in a stable position within the narrow flow field as the heart beats[13]. Thermodilution technique could estimate the mean flow rate from the saline bolus transit time[23]. However, it is affected by the quality of the injections and coronary flow rate (especially at high flow rates)[13]. Though this method was recently improved by continuous thermodilution with intracoronary saline infusion method, it is still difficult to guarantee adequate mixing of saline and blood[24]. Moreover, dual-sensor wires were required when using either Doppler or thermodilution to assess cMVR, greatly increasing the complexity of the measurements and limiting its in-hospital utilization.

Recently, image-based CFD method was used to simplify the procedure for assessing cMVR. Since blood flow was governed by the Navier-Stokes equations, the pressure or flow rate could be derived from each other with appropriate CFD model. One approach was to estimate the flow rate with TIMI frame count method and then calculate the corresponding pressure gradient from the reconstructed 3D model of the epicardial coronary arteries by using CFD method. With this approach, the cMVR was assessed with only measuring the aortic pressure during hyperemia condition[25, 26]. Moreover, by employing physiological models describing the relationship of mean flow rate or mean pressure during rest and hyperemia conditions, the cMVR was assessed without hyperemia[27]. This approach provided a simple estimation of cMVR by only measuring the aortic pressure, however, since TIMI frame count was a semi-quantitative method, the accuracy needs to be further validated. In this paper, a novel CFD based model was used to combine coronary angiography images and FFR measurement for assessing the intracoronary flow rate. With this approach, the Q and AMR was derived from routine FFR measurement, avoiding the use of the complex dual-sensor equipment. The proposed method provided an easy and accurate way to assess the coronary microvascular resistance. From the results, both Q and AMR have the ability to distinguish patients with normal MPI from those with abnormal MPI, and would facilitate the accurate diagnosis of CMD.

Limitation

There are several limitations in this study. First, this was a retrospective study with limited sample size, and no cutoff value for Q and AMR was determined. Further study with pre-defined sample size is needed. Second, rest SPECT MPI was used to indicate normal or abnormal myocardium perfusion, and more sensitive MPI (eg. stress SPECT MPI) should be further employed. Third, this was an explorative study and to increase the number of patients, two types of patients were enrolled. Further study is warranted to assess the diagnostic performance in patients with different coronary syndrome.

Funding

This work was supported by the Clinical Research Project of Air Force Medical University (2022LC2208), New Clinical Technology and New Business of Xijing Hospital (XJGX15Y39), the Program for the National Natural Science Foundation of China (grant no. 62171371, 81570210), Bethune-Merck Diabetes Research Foundation (G2017044), Advanced Foundation" of Xinxin Cardiovascular Health Foundation in Suzhou Industrial Park (2019-CCAACCESS065), Xi'an Science and Technology Project (20YXYJ0003(4)).

Ethics declarations

Ethics approval and consent to participate

Approval from the institutional review board of Fourth Military Medical 117 University was obtained (KY20172019-1,XI’an,China), and the study protocol was performed following the Declaration of Helsinki principles.

Consent for publication

Written informed consent was obtained from each participant.

Competing interests

The authors have no potential competing interests to disclose.

Availability of Data and Materials

No datasets were generated or analysed during the current study.

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No competing interests reported.

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A novel CFD based method for assessing the coronary microvascular dysfunction: combining coronary angiography and routine fractional flow reserve Running Title: CFD based method for assessing the CMD

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